GARFO summaries

Author

cslovas

Summaries

Totals by state

Total by license category

Landings by state

License categories by state

Maine

New Hampshire

Massachusetts

Connecticut

Rhode Island

New York

New Jersey

Delaware

Maryland

Virginia

North Carolina

Unique license types per year

Averages

Average number of licenses per vessel over time

Average number of vessels registered in each state over time

Proportion of holdings by license group

It appears that the majority of license holdings report their principal port state as Massachusetts.

Z-score

# totals of license categories issued per year per state
garfo_holdings %>%
  group_by(PPST, AP_YEAR, category) %>%
  summarise(count = sum(COUNT)) -> total 

# by state
total %>% 
  group_by(PPST) %>% # grouped counts by state (i.e. all licenses issued in Connecticut)
  mutate(z_score = scale(count, center = T, scale = T)) -> z_by_state # calculate z-score across all license categories for each state 

ggplot(z_by_state) +
  geom_line(aes(x = AP_YEAR, y = z_score, color = category)) +
  scale_color_gmri() +
  facet_wrap(~PPST, ncol = 4) +
  ylim(c(-8,8)) +
  theme_gmri(strip.background = element_rect(fill = "transparent"),
             strip.text = element_text(color = "black"))

# by species
total %>%
  group_by(category) %>% # grouped counts by category (i.e. all multispecies permits issued in GARFO jusridiction)
  mutate(z_score = scale(count, center = T, scale = T)) -> z_by_category # calculate z-score across all states for each license category

ggplot(z_by_category) +
  geom_line(aes(x = AP_YEAR, y = z_score, color = PPST)) +
  scale_color_gmri() +
  facet_wrap(~category, ncol = 3) +
  ylim(c(-8,8)) +
  theme_gmri(strip.background = element_rect(fill = "transparent"),
             strip.text = element_text(color = "black"))

# manual calculation for comparison (x - xmean / sd)

calc_z <- function(x, x_mean, sd){
  return((x-x_mean)/sd)
}

# by state
total %>% 
  group_by(PPST) %>% 
  mutate(z_score_manual = calc_z(count,
                mean(count),
                sd(count))) -> manual_state

manual_state %>% 
  left_join(z_by_state) # -> z_by_state
# A tibble: 4,852 × 6
# Groups:   PPST [11]
   PPST  AP_YEAR category                  count z_score_manual z_score[,1]
   <chr>   <int> <chr>                     <int>          <dbl>       <dbl>
 1 CT       1996 Lobster                      46         0.0346      0.0346
 2 CT       1996 Multispecies                110         2.23        2.23  
 3 CT       1996 Quahog                        8        -1.27       -1.27  
 4 CT       1996 Scallop                      22        -0.790      -0.790 
 5 CT       1996 Sea scallop                   5        -1.37       -1.37  
 6 CT       1996 Squid/Mackerel/Butterfish    73         0.963       0.963 
 7 CT       1996 Summer flounder              57         0.413       0.413 
 8 CT       1996 Surf clam                    10        -1.20       -1.20  
 9 CT       1997 Black sea bass               27        -0.618      -0.618 
10 CT       1997 Lobster                      46         0.0346      0.0346
# ℹ 4,842 more rows
# by category
total %>% 
  group_by(category) %>% 
  mutate(z_score_manual = calc_z(count,
                mean(count),
                sd(count))) -> manual_category

manual_category %>% 
  left_join(z_by_category) # -> z_by_category
# A tibble: 4,852 × 6
# Groups:   category [18]
   PPST  AP_YEAR category                  count z_score_manual z_score[,1]
   <chr>   <int> <chr>                     <int>          <dbl>       <dbl>
 1 CT       1996 Lobster                      46         -0.577      -0.577
 2 CT       1996 Multispecies                110         -0.602      -0.602
 3 CT       1996 Quahog                        8         -0.611      -0.611
 4 CT       1996 Scallop                      22         -0.531      -0.531
 5 CT       1996 Sea scallop                   5         -0.777      -0.777
 6 CT       1996 Squid/Mackerel/Butterfish    73         -0.765      -0.765
 7 CT       1996 Summer flounder              57         -0.668      -0.668
 8 CT       1996 Surf clam                    10         -0.556      -0.556
 9 CT       1997 Black sea bass               27         -0.979      -0.979
10 CT       1997 Lobster                      46         -0.577      -0.577
# ℹ 4,842 more rows